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Freeman chain code (FCC) representation in signature fraud detection based on nearest neighbour and artificial neural network (ANN) classifiers.

This paper presents a signature verification system that used Freeman Chain Code (FCC) as directional feature and data representation. There are 47 features were extracted from the signature images from six global features. Before extracting the features, the raw images were undergoing pre-processin...

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Main Authors: Azmi, Aini Najwa, Nasien, Dewi
Format: Article
Published: IJIP 2014
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.utm.my/id/eprint/59760/
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spelling my.utm.597602022-04-24T06:16:58Z http://eprints.utm.my/id/eprint/59760/ Freeman chain code (FCC) representation in signature fraud detection based on nearest neighbour and artificial neural network (ANN) classifiers. Azmi, Aini Najwa Nasien, Dewi QA75 Electronic computers. Computer science This paper presents a signature verification system that used Freeman Chain Code (FCC) as directional feature and data representation. There are 47 features were extracted from the signature images from six global features. Before extracting the features, the raw images were undergoing pre-processing stages which were binarization, noise removal by using media filter, cropping and thinning to produce Thinned Binary Image (TBI). Euclidean distance is measured and matched between nearest neighbours to find the result. MCYT-SignatureOff-75 database was used. Based on our experiment, the lowest FRR achieved is 6.67% and lowest FAR is 12.44% with only 1.12 second computational time from nearest neighbour classifier. The results are compared with Artificial Neural Network (ANN) classifier. IJIP 2014 Article PeerReviewed Azmi, Aini Najwa and Nasien, Dewi (2014) Freeman chain code (FCC) representation in signature fraud detection based on nearest neighbour and artificial neural network (ANN) classifiers. International Journal of Image Processing, 8 (6). pp. 434-454. ISSN 1985-2304
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Azmi, Aini Najwa
Nasien, Dewi
Freeman chain code (FCC) representation in signature fraud detection based on nearest neighbour and artificial neural network (ANN) classifiers.
description This paper presents a signature verification system that used Freeman Chain Code (FCC) as directional feature and data representation. There are 47 features were extracted from the signature images from six global features. Before extracting the features, the raw images were undergoing pre-processing stages which were binarization, noise removal by using media filter, cropping and thinning to produce Thinned Binary Image (TBI). Euclidean distance is measured and matched between nearest neighbours to find the result. MCYT-SignatureOff-75 database was used. Based on our experiment, the lowest FRR achieved is 6.67% and lowest FAR is 12.44% with only 1.12 second computational time from nearest neighbour classifier. The results are compared with Artificial Neural Network (ANN) classifier.
format Article
author Azmi, Aini Najwa
Nasien, Dewi
author_facet Azmi, Aini Najwa
Nasien, Dewi
author_sort Azmi, Aini Najwa
title Freeman chain code (FCC) representation in signature fraud detection based on nearest neighbour and artificial neural network (ANN) classifiers.
title_short Freeman chain code (FCC) representation in signature fraud detection based on nearest neighbour and artificial neural network (ANN) classifiers.
title_full Freeman chain code (FCC) representation in signature fraud detection based on nearest neighbour and artificial neural network (ANN) classifiers.
title_fullStr Freeman chain code (FCC) representation in signature fraud detection based on nearest neighbour and artificial neural network (ANN) classifiers.
title_full_unstemmed Freeman chain code (FCC) representation in signature fraud detection based on nearest neighbour and artificial neural network (ANN) classifiers.
title_sort freeman chain code (fcc) representation in signature fraud detection based on nearest neighbour and artificial neural network (ann) classifiers.
publisher IJIP
publishDate 2014
url http://eprints.utm.my/id/eprint/59760/
_version_ 1732945377545420800
score 13.209306

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